According to the statistics provided by the Department of Statistics, Ministry of Transportation and Communications, Republic of China, the primary cause of the A-1 class fatal traffic accidents in Taiwan had been “careless driving” for three successive years, i.e. from year 2001 to year 2003. It is, therefore, concluded that most traffic accidents are caused by human errors. That is, a traffic accident can simply be resulted from a tried, a careless, or a distracted driver, since it is hard for a driver to keep alert all the time as she/he can be easily distracted by all kinds of things. Drivers that are easily distracted and careless usually are not capable of driving his/her way out of an accident.
Hence, a driving assistance system capable of detecting and tracking traffic lane in real time for measuring an amount of deviation of a vehicle carrying the system can be very helpful for accident prevention. One such system is disclosed in EPC Pat. No. WO2005023588, entitled “Detection of Unintended Lane Departures”, which employs an image processing and identification technique for evaluating lane departures of a vehicle and thus issuing a alarm to alert the driver if an abnormal lane departure is detected.
However, although the aforesaid method can assist a drive to avoid the condition of unintended lane departures, the accuracy of its evaluation can be adversely affected by weathers. For instance, when driving in a raining night and water begins to accumulate on the road, images of the road captured by cameras of the aforesaid method might be contaminated by the light of the street lamps or vehicle lamps reflected from water patches on the road, and thus the detection of the aforesaid method based on such contaminated images could be erroneous. It is noted that such weather-affected errors could happen no matter the camera is using charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) device for image capturing.
Therefore, it is in need of a lane departure warning method and apparatus for automatically detecting unintended lane departures of a vehicle that is capable of accurately interpreting images affected by weather or ambient environment of the vehicle and thus is free from making erroneous judgments similar to the prior arts.